Emotion Recognition of EMG Based on Improved L-M BP Neural Network and SVM

Yang, Shanxiao; Yang, Guijuan

doi:10.4304/jsw.6.8.1529-1536

Cited by 27 publications

(14 citation statements)

References 9 publications

(10 reference statements)

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“…The authors decomposed signals by discrete wavelet transform (DWT) to select maxima and minima of the wavelet coefficients and got a total recognition rate of 75% by BP neural network with mere EMG. Study [ 84 ] used the same features to classify four emotions and got a recognition rate of 85% by support vector machine (SVM). Another study [ 7 ] for emotion recognition is proposed based on the EMG.…”

Section: Emotional Relevant Features Of Physiological Signalsmentioning

confidence: 99%

A Review of Emotion Recognition Using Physiological Signals

Shu

Xie

Yang

et al. 2018

Sensors

622

352

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Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.

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Section: Emotional Relevant Features Of Physiological Signalsmentioning

confidence: 99%

A Review of Emotion Recognition Using Physiological Signals

Shu

Xie

Yang

et al. 2018

Sensors

622

352

View full text Add to dashboard Cite

show abstract

“…In addition, Chinese emotion expression is subtle and the features of micro movie reviews are sparse. Moreover, it needs to be noted that the evaluating indices of M1 are higher than that of M2 while using MNB, PRM, SVM [12][13] and M-PRM for testing; it shows that M1 is more popular than M2. But the values of these indices are reverse by SWM, because objective sentiment describing plots may contain in the reviews.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Sentiment Recognition of Online Chinese Micro Movie Reviews Using Multiple Probabilistic Reasoning Model

Xu¹,

Li²,

Wang³

et al. 2013

JCP

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Online review websites provide an important channel for people to share their opinions. In this paper, we research the sentiment recognition technology on online Chinese micro movie reviews. As the sentiment expressed in Chinese is subtle and the feature space is very sparse, we adopt n-grams to develop sentiment feature space, and then propose an ensemble learning algorithm based on random feature space division method, namely Multiple Probabilistic Reasoning Model (M-PRM), for supervised document level sentiment classification. This algorithm captures discriminative sentiment features and makes full use of them. Comparing with this algorithm, we apply other four machine learning methods: Multinomial NaiveBayes (MNB), Probabilistic Reasoning Model (PRM), Sentimentword method (SWM) and SVM on two micro movie review datasets. Results show that M-PRM achieves better classification performance than other methods.

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“…Back Propagation(BP) network is a typical Artificial Neural Network, which is widely used emotional computing [11][12] [13]. It is simple and efficient, furthermore, it has a low resources consumption, but it still has many disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Speech-oriented negative emotion recognition

Zhao

2015

2015 34th Chinese Control Conference (CCC)

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Standard Back Propagation(BP) network is easily trapped into a local optimal solution. Two main approaches are commonly used to improve its appearance. One is to employ numerical optimization methods, this approach is simple and fast, but severe with computational storage, in addition could not guarantee convergence. Another is to employ gradient descent methods, this approach can achieve a global minimum with high probability, but more likely to cause oscillations, and the parameters are hard to determine. Motivated by the innovation character of human being, a simulation of this psychology phenomena is proposed to raise the probability of obtaining a global optimization and reducing oscillations, then a combination of the genetic algorithm and this innovation mechanism is introduced to deal with the initialization.

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Emotion Recognition of EMG Based on Improved L-M BP Neural Network and SVM

Cited by 27 publications

References 9 publications

A Review of Emotion Recognition Using Physiological Signals

A Review of Emotion Recognition Using Physiological Signals

Sentiment Recognition of Online Chinese Micro Movie Reviews Using Multiple Probabilistic Reasoning Model

Speech-oriented negative emotion recognition

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